Deep learning has exhibited exceptional effectiveness in predicting the rupture risk of intracranial aneurysms(IA). Our study aimed to provide a valuable tool for extracting Deep Learning features and predicting the rupture risk of intracranial aneurysms. To fulfill this objective, we meticulously developed a deep learning feature extraction framework to extract deep learning features from CTA images and precisely forecast the rupture risk of intracranial aneurysms. The study included 327 IAs in the training dataset and 82 in the test dataset. A model using ResNet-34 demonstrated outstanding performance, achieving an accuracy of 87.65% (80.49%-94.82%), a recall of 98.33% (95.55%-98.74%), and an AUC of 84.52% (76.65%-92.40%). Our results showcase that assessing aneurysm rupture risk performs well when utilizing deep learning features extracted through our developed framework.